Summary

This research project explores how Large Language Models (LLMs) can be enhanced using Retrieval-Augmented Generation (RAG) techniques. By combining pretrained models with external, up-to-date knowledge sources, RAG systems allow for more accurate, contextual, and domain-specific responses. The focus is on how to architect and implement scalable RAG-based solutions using open-source tools.

Objectives

• Understand the components and architecture of a RAG system.
• Explore vector databases and semantic search for contextual querying.
• Evaluate trade-offs between fine-tuned vs retrieval-augmented models.
• Prototype a modular RAG pipeline using current tooling.

Core Topics Covered

• How transformers work: tokenization, embeddings, and attention mechanisms.
• Generalization vs memorization in LLMs.
• Embedding generation and cosine similarity for semantic search.
• Chunking strategies for better retrieval accuracy.
• Latency, relevance, and scale in production RAG systems.
• Enterprise use cases in insurance, legal, support, and compliance.

Tools & Stack

• LLM: Ollama with CodeLlama or GPT variants
• Embedding Model: OpenAI or HuggingFace Sentence Transformers
• Vector Store: Qdrant or Weaviate
• Framework: LangChain or custom .NET pipeline
• Integration: Node/REST APIs, local file ingestion (PDF, CSV, Markdown)

Work in Progress

• Testing different chunking strategies for precision in long documents.
• Building a hybrid cache layer to improve retrieval latency.
• Designing a multi-tenant RAG architecture for enterprise clients.

Deliverables (Coming Soon)

• System architecture diagram for a scalable RAG setup.
• Code samples in .NET and Node.js.
• Performance benchmarks: LLM-only vs RAG-enhanced.
• Optional blog series or downloadable PDF whitepaper.